Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations1460
Missing cells3580
Missing cells (%)10.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory513.8 KiB
Average record size in memory360.4 B

Variable types

Numeric20
Categorical4

Alerts

2ndFlrSF has 86 (5.9%) missing values Missing
BedroomAbvGr has 99 (6.8%) missing values Missing
BsmtExposure has 38 (2.6%) missing values Missing
BsmtFinType1 has 145 (9.9%) missing values Missing
EnclosedPorch has 1324 (90.7%) missing values Missing
GarageFinish has 235 (16.1%) missing values Missing
GarageYrBlt has 81 (5.5%) missing values Missing
LotFrontage has 259 (17.7%) missing values Missing
WoodDeckSF has 1305 (89.4%) missing values Missing
2ndFlrSF has 781 (53.5%) zeros Zeros
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
EnclosedPorch has 116 (7.9%) zeros Zeros
GarageArea has 81 (5.5%) zeros Zeros
MasVnrArea has 861 (59.0%) zeros Zeros
OpenPorchSF has 656 (44.9%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
WoodDeckSF has 78 (5.3%) zeros Zeros

Reproduction

Analysis started2025-03-25 22:49:13.039430
Analysis finished2025-03-25 22:50:12.941455
Duration59.9 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:13.065547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2025-03-25T23:50:13.253528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

Missing  Zeros 

Distinct401
Distinct (%)29.2%
Missing86
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean348.52402
Minimum0
Maximum2065
Zeros781
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:13.435566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1142
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation438.86559
Coefficient of variation (CV)1.2592119
Kurtosis-0.54642813
Mean348.52402
Median Absolute Deviation (MAD)0
Skewness0.81512331
Sum478872
Variance192603
MonotonicityNot monotonic
2025-03-25T23:50:13.613459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 781
53.5%
728 10
 
0.7%
672 8
 
0.5%
504 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
546 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
840 5
 
0.3%
Other values (391) 530
36.3%
(Missing) 86
 
5.9%
ValueCountFrequency (%)
0 781
53.5%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 1
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

BedroomAbvGr
Real number (ℝ)

Missing 

Distinct8
Distinct (%)0.6%
Missing99
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean2.8692138
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:13.772579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8201148
Coefficient of variation (CV)0.28583258
Kurtosis2.3183658
Mean2.8692138
Median Absolute Deviation (MAD)0
Skewness0.22954095
Sum3905
Variance0.67258828
MonotonicityNot monotonic
2025-03-25T23:50:13.982535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 749
51.3%
2 333
22.8%
4 199
 
13.6%
1 46
 
3.2%
5 20
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
(Missing) 99
 
6.8%
ValueCountFrequency (%)
0 6
 
0.4%
1 46
 
3.2%
2 333
22.8%
3 749
51.3%
4 199
 
13.6%
5 20
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 20
 
1.4%
4 199
 
13.6%
3 749
51.3%
2 333
22.8%
1 46
 
3.2%
0 6
 
0.4%

BsmtExposure
Categorical

Missing 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size73.0 KiB
No
953 
Av
221 
Gd
134 
Mn
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 953
65.3%
Av 221
 
15.1%
Gd 134
 
9.2%
Mn 114
 
7.8%
(Missing) 38
 
2.6%

Length

2025-03-25T23:50:14.149573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T23:50:14.287567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
no 953
67.0%
av 221
 
15.5%
gd 134
 
9.4%
mn 114
 
8.0%

Most occurring characters

ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

BsmtFinSF1
Real number (ℝ)

Zeros 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:14.443092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2025-03-25T23:50:14.625803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType1
Categorical

Missing 

Distinct6
Distinct (%)0.5%
Missing145
Missing (%)9.9%
Memory size74.8 KiB
Unf
396 
GLQ
385 
ALQ
202 
BLQ
136 
Rec
126 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3945
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 396
27.1%
GLQ 385
26.4%
ALQ 202
13.8%
BLQ 136
 
9.3%
Rec 126
 
8.6%
LwQ 70
 
4.8%
(Missing) 145
 
9.9%

Length

2025-03-25T23:50:14.801296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T23:50:14.955293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
unf 396
30.1%
glq 385
29.3%
alq 202
15.4%
blq 136
 
10.3%
rec 126
 
9.6%
lwq 70
 
5.3%

Most occurring characters

ValueCountFrequency (%)
L 793
20.1%
Q 793
20.1%
U 396
10.0%
n 396
10.0%
f 396
10.0%
G 385
9.8%
A 202
 
5.1%
B 136
 
3.4%
R 126
 
3.2%
e 126
 
3.2%
Other values (2) 196
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 793
20.1%
Q 793
20.1%
U 396
10.0%
n 396
10.0%
f 396
10.0%
G 385
9.8%
A 202
 
5.1%
B 136
 
3.4%
R 126
 
3.2%
e 126
 
3.2%
Other values (2) 196
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 793
20.1%
Q 793
20.1%
U 396
10.0%
n 396
10.0%
f 396
10.0%
G 385
9.8%
A 202
 
5.1%
B 136
 
3.4%
R 126
 
3.2%
e 126
 
3.2%
Other values (2) 196
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 793
20.1%
Q 793
20.1%
U 396
10.0%
n 396
10.0%
f 396
10.0%
G 385
9.8%
A 202
 
5.1%
B 136
 
3.4%
R 126
 
3.2%
e 126
 
3.2%
Other values (2) 196
 
5.0%

BsmtUnfSF
Real number (ℝ)

Zeros 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:15.145088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2025-03-25T23:50:15.327253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

EnclosedPorch
Real number (ℝ)

Missing  Zeros 

Distinct19
Distinct (%)14.0%
Missing1324
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean25.330882
Minimum0
Maximum286
Zeros116
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:15.477260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile218
Maximum286
Range286
Interquartile range (IQR)0

Descriptive statistics

Standard deviation66.684115
Coefficient of variation (CV)2.6325224
Kurtosis5.4086006
Mean25.330882
Median Absolute Deviation (MAD)0
Skewness2.5762649
Sum3445
Variance4446.7712
MonotonicityNot monotonic
2025-03-25T23:50:15.794566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 116
 
7.9%
112 2
 
0.1%
244 2
 
0.1%
50 1
 
0.1%
286 1
 
0.1%
42 1
 
0.1%
145 1
 
0.1%
190 1
 
0.1%
226 1
 
0.1%
138 1
 
0.1%
Other values (9) 9
 
0.6%
(Missing) 1324
90.7%
ValueCountFrequency (%)
0 116
7.9%
42 1
 
0.1%
50 1
 
0.1%
91 1
 
0.1%
112 2
 
0.1%
136 1
 
0.1%
138 1
 
0.1%
144 1
 
0.1%
145 1
 
0.1%
158 1
 
0.1%
ValueCountFrequency (%)
286 1
0.1%
268 1
0.1%
244 2
0.1%
234 1
0.1%
226 1
0.1%
224 1
0.1%
216 1
0.1%
190 1
0.1%
185 1
0.1%
158 1
0.1%

GarageArea
Real number (ℝ)

Zeros 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:16.040636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2025-03-25T23:50:16.309636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

GarageFinish
Categorical

Missing 

Distinct3
Distinct (%)0.2%
Missing235
Missing (%)16.1%
Memory size75.2 KiB
Unf
546 
RFn
366 
Fin
313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3675
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 546
37.4%
RFn 366
25.1%
Fin 313
21.4%
(Missing) 235
16.1%

Length

2025-03-25T23:50:16.540673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T23:50:16.718129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
unf 546
44.6%
rfn 366
29.9%
fin 313
25.6%

Most occurring characters

ValueCountFrequency (%)
n 1225
33.3%
F 679
18.5%
U 546
14.9%
f 546
14.9%
R 366
 
10.0%
i 313
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1225
33.3%
F 679
18.5%
U 546
14.9%
f 546
14.9%
R 366
 
10.0%
i 313
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1225
33.3%
F 679
18.5%
U 546
14.9%
f 546
14.9%
R 366
 
10.0%
i 313
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1225
33.3%
F 679
18.5%
U 546
14.9%
f 546
14.9%
R 366
 
10.0%
i 313
 
8.5%

GarageYrBlt
Real number (ℝ)

Missing 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.5062
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:16.925129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.689725
Coefficient of variation (CV)0.012478973
Kurtosis-0.418341
Mean1978.5062
Median Absolute Deviation (MAD)21
Skewness-0.64941462
Sum2728360
Variance609.58251
MonotonicityNot monotonic
2025-03-25T23:50:17.170092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 65
 
4.5%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
2008 29
 
2.0%
Other values (87) 949
65.0%
(Missing) 81
 
5.5%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
 
0.2%
1914 2
 
0.1%
1915 2
 
0.1%
1916 5
 
0.3%
1918 2
 
0.1%
1920 14
1.0%
1921 3
 
0.2%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
2.0%
2007 49
3.4%
2006 59
4.0%
2005 65
4.5%
2004 53
3.6%
2003 50
3.4%
2002 26
 
1.8%
2001 20
 
1.4%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:17.422091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2025-03-25T23:50:17.656502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

KitchenQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size72.8 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 735
50.3%
Gd 586
40.1%
Ex 100
 
6.8%
Fa 39
 
2.7%

Length

2025-03-25T23:50:17.865472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-25T23:50:18.043496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 735
50.3%
gd 586
40.1%
ex 100
 
6.8%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:18.286461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2025-03-25T23:50:18.479452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

LotFrontage
Real number (ℝ)

Missing 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:18.659242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2025-03-25T23:50:18.854243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:19.050271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2025-03-25T23:50:19.244304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.0%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
200 6
 
0.4%
Other values (317) 529
36.2%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 861
59.0%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

OpenPorchSF
Real number (ℝ)

Zeros 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:19.462307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2025-03-25T23:50:19.703862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:19.894827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2025-03-25T23:50:20.073866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:20.264828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2025-03-25T23:50:20.415879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

TotalBsmtSF
Real number (ℝ)

Zeros 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:20.571314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2025-03-25T23:50:20.761865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

WoodDeckSF
Real number (ℝ)

Missing  Zeros 

Distinct58
Distinct (%)37.4%
Missing1305
Missing (%)89.4%
Infinite0
Infinite (%)0.0%
Mean103.74194
Minimum0
Maximum736
Zeros78
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:20.947531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3182.5
95-th percentile355.3
Maximum736
Range736
Interquartile range (IQR)182.5

Descriptive statistics

Standard deviation135.54315
Coefficient of variation (CV)1.3065416
Kurtosis2.7810699
Mean103.74194
Median Absolute Deviation (MAD)0
Skewness1.5114787
Sum16080
Variance18371.946
MonotonicityNot monotonic
2025-03-25T23:50:21.320561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
5.3%
192 5
 
0.3%
120 4
 
0.3%
100 4
 
0.3%
144 3
 
0.2%
216 2
 
0.1%
196 2
 
0.1%
300 2
 
0.1%
240 2
 
0.1%
160 2
 
0.1%
Other values (48) 51
 
3.5%
(Missing) 1305
89.4%
ValueCountFrequency (%)
0 78
5.3%
24 1
 
0.1%
33 1
 
0.1%
36 1
 
0.1%
44 1
 
0.1%
78 1
 
0.1%
84 1
 
0.1%
100 4
 
0.3%
104 1
 
0.1%
105 1
 
0.1%
ValueCountFrequency (%)
736 1
0.1%
550 1
0.1%
466 1
0.1%
431 1
0.1%
416 1
0.1%
382 1
0.1%
364 1
0.1%
356 1
0.1%
355 1
0.1%
351 1
0.1%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:21.502702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2025-03-25T23:50:21.695846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:21.876123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2025-03-25T23:50:22.071121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2025-03-25T23:50:22.257205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2025-03-25T23:50:22.454454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

2025-03-25T23:50:08.877153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:13.600152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:16.860658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:19.690484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:22.945945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:26.063993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:28.843856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:31.715974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:34.644278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.445096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.306578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:43.039332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:45.799471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:48.907386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:51.710972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:54.585248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:57.476881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:00.297215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:03.162280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:06.039863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:09.006488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:13.718965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:26.190338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:28.984859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:31.856013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:34.768165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.571188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.426580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:43.158956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:45.928417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:51.836974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:57.607535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:50:06.164867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:09.143489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:29.116772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:31.986851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:34.898165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.698852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:43.290990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:46.071381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:49.156663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:50:00.573864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:14.236963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:17.263083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:20.106908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:23.355876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:26.526350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:29.241803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:32.140657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:35.033161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.839159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.712152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:43.443981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:46.226378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:49.307761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:52.119937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:54.978867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:57.893233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:00.705305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:03.580703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:06.461409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:09.442040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:14.404968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:17.402057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:20.249804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:23.493917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:26.669847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:29.382771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:32.282239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:35.167161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.975158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.852151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:43.578723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:46.360378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:49.437758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:52.266943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:55.281496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:58.027229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:00.847865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:03.725185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:06.608062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:09.641973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:14.567569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:17.538502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:20.393801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:23.624656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:26.789847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:29.505877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:32.422824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:35.303180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:38.097203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.989149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:43.716868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:46.501416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:49.569685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:52.399951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:55.399469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:58.160192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:00.984867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:03.860185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:06.735635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:09.769048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:14.771250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:38.228157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:27.058532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:29.788534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:32.700337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-25T23:49:39.889108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:42.606013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:45.374014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:48.437400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:51.268869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:54.108033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:57.061882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:59.841215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:02.777280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:05.602757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:08.422127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:11.654833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:16.561487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:19.409513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:22.610225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:25.742821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:28.562877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:31.410863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:34.340554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.155230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.025614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:42.750333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:45.508016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:48.589758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:51.408887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:54.252007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:57.198911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:59.984252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:02.899282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:05.744493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:08.570754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:11.799839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:16.715360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:19.543505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:22.784880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:25.905996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:28.701820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:31.557494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:34.489054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:37.301210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:40.160609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:42.891332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:45.657414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:48.750386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:51.563950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:54.401029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:49:57.339858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:00.141247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:03.033312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:05.888863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-25T23:50:08.722188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2025-03-25T23:50:12.039830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-25T23:50:12.475184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-25T23:50:12.777289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

1stFlrSF2ndFlrSFBedroomAbvGrBsmtExposureBsmtFinSF1BsmtFinType1BsmtUnfSFEnclosedPorchGarageAreaGarageFinishGarageYrBltGrLivAreaKitchenQualLotAreaLotFrontageMasVnrAreaOpenPorchSFOverallCondOverallQualTotalBsmtSFWoodDeckSFYearBuiltYearRemodAddSalePrice
0856854.03.0No706GLQ1500.0548RFn2003.01710Gd845065.0196.061578560.020032003208500
112620.03.0Gd978ALQ284NaN460RFn1976.01262TA960080.00.00861262NaN19761976181500
2920866.03.0Mn486GLQ4340.0608RFn2001.01786Gd1125068.0162.04257920NaN20012002223500
3961NaNNaNNo216ALQ540NaN642Unf1998.01717Gd955060.00.03557756NaN19151970140000
41145NaN4.0Av655GLQ4900.0836RFn2000.02198Gd1426084.0350.084581145NaN20002000250000
5796566.01.0No732GLQ64NaN480Unf1993.01362TA1411585.00.03055796NaN19931995143000
616940.03.0Av1369GLQ317NaN636RFn2004.01694Gd1008475.0186.057581686NaN20042005307000
71107983.03.0Mn859ALQ216NaN484NaN1973.02090TA10382NaN240.0204671107NaN19731973200000
81022752.02.0No0Unf952NaN468Unf1931.01774TA612051.00.0057952NaN19311950129900
910770.02.0No851GLQ140NaN205RFn1939.01077TA742050.00.0465991NaN19391950118000
1stFlrSF2ndFlrSFBedroomAbvGrBsmtExposureBsmtFinSF1BsmtFinType1BsmtUnfSFEnclosedPorchGarageAreaGarageFinishGarageYrBltGrLivAreaKitchenQualLotAreaLotFrontageMasVnrAreaOpenPorchSFOverallCondOverallQualTotalBsmtSFWoodDeckSFYearBuiltYearRemodAddSalePrice
1450896896.04.0No0Unf896NaN0NaNNaN1792TA900060.00.04555896NaN19741974136000
145115780.03.0No0Unf1573NaN840NaN2008.01578Ex926278.0194.036581573NaN20082009287090
145210720.02.0Gd547GLQ0NaN525Fin2005.01072TA367535.080.02855547NaN20052005145000
145311400.03.0No0Unf11400.00NaNNaN1140TA1721790.00.056551140NaN2006200684500
145412210.02.0No410GLQ8110.0400RFn2004.01221Gd750062.00.0113571221NaN20042005185000
1455953694.03.0No0Unf953NaN460RFn1999.01647TA791762.00.040569530.019992000175000
145620730.0NaNNo790ALQ589NaN500Unf1978.02073TA1317585.0119.00661542NaN19781988210000
145711881152.04.0No275GLQ877NaN252RFn1941.02340Gd904266.00.060971152NaN19412006266500
145810780.02.0Mn49NaN0112.0240Unf1950.01078Gd971768.00.00651078NaN19501996142125
145912560.03.0No830BLQ1360.0276Fin1965.01256TA993775.00.068651256736.019651965147500